Tomato is a proof-of-concept for a steganography tool that utilizes the minimum entropy coupling code provided by ssokota. This tool achieves information concealment by merging the probability distribution of hidden information (ciphertext) with that of cover text generated by large language models (LLM). This coupling minimizes joint entropy, ensuring that the stego text (cover text combined with embedded information) retains the statistical features of natural language, making hidden information difficult to detect. During the decoding process, LLM assists by providing context-aware explanations, and then uses MEC to decouple the hidden information from the cover text. This method ensures that hidden information can be seamlessly integrated into the text and retrieved safely and accurately later, minimizing risk.